Being over-parameterized, deep neural networks have significant power to represent complex data distributions [1]}, [2]}, [3]}. On the other hand, their capacity renders them susceptible to overfiting and data memorization [4]}. As a result, studying and analyzing the overfitting and memorizing behaviour of deep neural networks is an active field of research [5]}, [6]}, [7]}. This includes the study of generalization and the generalization gap [8]}, [9]}, [10]}, [11]}, which is related to our work. In [8]}, [9]}, the authors examine the generalization gap in the context of the training and test losses. This provides a good overall assessment of the model's generalization at the classification level. In contrast, our work focuses on the generalization of the feature extraction layers, which we deem to be a prerequisite for good generalization at the classifier level.
